Wall temperature prediction at critical heat flux using a machine learning model

被引:39
作者
Park, Hae Min [1 ]
Lee, Jong Hyuk [2 ]
Kim, Kyung Doo [2 ]
机构
[1] Korea Atom Energy Res Inst, Maritime Reactor Dev Div, 111 Daedeok Daero 989 Beon Gil, Daejeon 34057, South Korea
[2] Korea Atom Energy Res Inst, Reactor Syst Safety Res Div, 111 Daedeok Daero 989 Beon Gil, Daejeon 34057, South Korea
关键词
Wall temperature; Critical heat flux; Machine learning; SPACE code; CHF;
D O I
10.1016/j.anucene.2020.107334
中图分类号
TL [原子能技术]; O571 [原子核物理学];
学科分类号
0827 ; 082701 ;
摘要
To determine heat transfer regimes of the pre and post CHF, the SPACE code calculates the wall temperature from a nucleate boiling heat transfer model at the given CHF. It needs iterations and consumes a large amount of computing time. To reduce the calculation time, this paper introduces the application of a machine learning method. Big data of the wall temperature at CHF was built by using the subprogram constructed as is in the SPACE code. Based on that database, the neural network models were trained and two neural network models having different configurations were suggested. The developed neural network models were implemented in the SPACE code and test calculations were performed. The neural network applied SPACE code properly predicted the wall temperature at CHF. In test calculations, the calculation time was also investigated. All suggested neural network models highly enhanced the calculation speed corresponding to a maximum 86% time reduction. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页数:9
相关论文
共 24 条
[1]  
Abadi M, 2016, TENSORFLOW SYSTEM LA
[2]  
[Anonymous], IDSIA0412
[3]  
[Anonymous], S06NX08K1TR36 KHNP K
[4]  
[Anonymous], MULT MOD VAL WORKSH
[5]  
[Anonymous], AERER5373
[6]  
[Anonymous], NP1460 EPRI
[7]  
[Anonymous], J CO 2 UTIL
[8]  
[Anonymous], THESIS
[9]  
[Anonymous], ML071000097 USNRC
[10]  
Behnke S., 2003, Hierarchical Neural Networks for Image Interpretation